{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-02-15T07:07:02.853942Z",
     "start_time": "2025-02-15T07:06:56.817455Z"
    }
   },
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.decomposition import PCA\n",
    "import jieba\n",
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "source": [
    "def dictvec():\n",
    "    dict1 = DictVectorizer(sparse=True)\n",
    "    data = dict1.fit_transform([{'city': '北京', 'temperature': 100},\n",
    "                                {'city': '上海', 'temperature': 60},\n",
    "                                {'city': '深圳', 'temperature': 30}])\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(dict1.get_feature_names_out())\n",
    "    print('-' * 50)\n",
    "    print(dict1.inverse_transform(data))\n",
    "    return None\n",
    "\n",
    "\n",
    "dictvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T12:20:52.324845Z",
     "start_time": "2025-01-16T12:20:52.320552Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Compressed Sparse Row sparse matrix of dtype 'float64'\n",
      "\twith 6 stored elements and shape (3, 4)>\n",
      "  Coords\tValues\n",
      "  (0, 1)\t1.0\n",
      "  (0, 3)\t100.0\n",
      "  (1, 0)\t1.0\n",
      "  (1, 3)\t60.0\n",
      "  (2, 2)\t1.0\n",
      "  (2, 3)\t30.0\n",
      "--------------------------------------------------\n",
      "['city=上海' 'city=北京' 'city=深圳' 'temperature']\n",
      "--------------------------------------------------\n",
      "[{'city=北京': np.float64(1.0), 'temperature': np.float64(100.0)}, {'city=上海': np.float64(1.0), 'temperature': np.float64(60.0)}, {'city=深圳': np.float64(1.0), 'temperature': np.float64(30.0)}]\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "source": [
    "def couvec():\n",
    "    vector = CountVectorizer()\n",
    "    res = vector.fit_transform(\n",
    "        [\"life is  short,i like python life\",\n",
    "         \"life is too long,i dislike python\",\n",
    "         \"life is short\"])\n",
    "    print(vector.get_feature_names_out())\n",
    "    print('-' * 50)\n",
    "    print(res)\n",
    "    print('-' * 50)\n",
    "    print(type(res))\n",
    "    print('-' * 50)\n",
    "    print(res.toarray())\n",
    "    print('-' * 50)\n",
    "    print(vector.inverse_transform(res))\n",
    "\n",
    "\n",
    "couvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T12:33:14.178463Z",
     "start_time": "2025-01-16T12:33:14.173524Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dislike' 'is' 'life' 'like' 'long' 'python' 'short' 'too']\n",
      "--------------------------------------------------\n",
      "<Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 14 stored elements and shape (3, 8)>\n",
      "  Coords\tValues\n",
      "  (0, 2)\t2\n",
      "  (0, 1)\t1\n",
      "  (0, 6)\t1\n",
      "  (0, 3)\t1\n",
      "  (0, 5)\t1\n",
      "  (1, 2)\t1\n",
      "  (1, 1)\t1\n",
      "  (1, 5)\t1\n",
      "  (1, 7)\t1\n",
      "  (1, 4)\t1\n",
      "  (1, 0)\t1\n",
      "  (2, 2)\t1\n",
      "  (2, 1)\t1\n",
      "  (2, 6)\t1\n",
      "--------------------------------------------------\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n",
      "[[0 1 2 1 0 1 1 0]\n",
      " [1 1 1 0 1 1 0 1]\n",
      " [0 1 1 0 0 0 1 0]]\n",
      "--------------------------------------------------\n",
      "[array(['life', 'is', 'short', 'like', 'python'], dtype='<U7'), array(['life', 'is', 'python', 'too', 'long', 'dislike'], dtype='<U7'), array(['life', 'is', 'short'], dtype='<U7')]\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "source": [
    "def countvec():\n",
    "    cv = CountVectorizer()\n",
    "    data = cv.fit_transform([\"人生苦短，我 喜欢 python python\", \"人生漫长，不用 python\"])\n",
    "    print(cv.get_feature_names_out())\n",
    "    print('-' * 50)\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(data.toarray())\n",
    "    return None\n",
    "\n",
    "\n",
    "countvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T12:46:33.507453Z",
     "start_time": "2025-01-16T12:46:33.502948Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['python' '不用' '人生漫长' '人生苦短' '喜欢']\n",
      "--------------------------------------------------\n",
      "<Compressed Sparse Row sparse matrix of dtype 'int64'\n",
      "\twith 6 stored elements and shape (2, 5)>\n",
      "  Coords\tValues\n",
      "  (0, 3)\t1\n",
      "  (0, 4)\t1\n",
      "  (0, 0)\t2\n",
      "  (1, 0)\t1\n",
      "  (1, 2)\t1\n",
      "  (1, 1)\t1\n",
      "--------------------------------------------------\n",
      "[[2 0 0 1 1]\n",
      " [1 1 1 0 0]]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "source": [
    "def cutword():\n",
    "    con1 = jieba.cut(\"今天很残酷，明天更残酷，后天很美好，但绝对大部分是死在明天晚上，所以每个人不要放弃今天。\")\n",
    "    con2 = jieba.cut(\"我们看到的从很远星系来的光是在几百万年之前发出的，这样当我们看到宇宙时，我们是在看它的过去。\")\n",
    "    con3 = jieba.cut(\n",
    "        \"如果只用一种方式了解某样事物，你就不会真正了解它。了解事物真正含义的秘密取决于如何将其与我们所了解的事物相联系。\")\n",
    "    print(type(con1))\n",
    "    print('-' * 50)\n",
    "    content1 = list(con1)\n",
    "    content2 = list(con2)\n",
    "    content3 = list(con3)\n",
    "    print(content1)\n",
    "    print(content2)\n",
    "    print(content3)\n",
    "    print('-' * 50)\n",
    "    c1 = ' '.join(content1)\n",
    "    c2 = ' '.join(content2)\n",
    "    c3 = ' '.join(content3)\n",
    "    return c1, c2, c3\n",
    "\n",
    "\n",
    "def hanzivec():\n",
    "    c1, c2, c3 = cutword()\n",
    "    print('-' * 50)\n",
    "    print(c1)\n",
    "    print(c2)\n",
    "    print(c3)\n",
    "    print('-' * 50)\n",
    "    cv = CountVectorizer()\n",
    "    data = cv.fit_transform([c1, c2, c3])\n",
    "    print(cv.get_feature_names_out())\n",
    "    print(data.toarray())\n",
    "    return None\n",
    "\n",
    "\n",
    "cutword()\n",
    "hanzivec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T13:07:37.582102Z",
     "start_time": "2025-01-16T13:07:37.575674Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'generator'>\n",
      "--------------------------------------------------\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝对', '大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "--------------------------------------------------\n",
      "<class 'generator'>\n",
      "--------------------------------------------------\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝对', '大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "--------------------------------------------------\n",
      "--------------------------------------------------\n",
      "今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝对 大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。\n",
      "我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。\n",
      "如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。\n",
      "--------------------------------------------------\n",
      "['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '大部分' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个'\n",
      " '看到' '真正' '秘密' '绝对' '美好' '联系' '过去' '这样']\n",
      "[[0 0 1 0 0 0 2 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 2 0 1 0 2 1 0 0 0 1 1 0 0 0]\n",
      " [0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 1 3 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 1 1]\n",
      " [1 1 0 0 4 3 0 0 0 0 1 1 0 1 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 2 1 0 0 1 0 0]]\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "source": [
    "def tfidfvec():\n",
    "    c1, c2, c3 = cutword()\n",
    "    print(c1, c2, c3)\n",
    "    tf = TfidfVectorizer(smooth_idf=True)\n",
    "    data = tf.fit_transform([c1, c2, c3])\n",
    "    print(tf.get_feature_names_out())\n",
    "    print('-' * 50)\n",
    "    print(type(data))\n",
    "    print('-' * 50)\n",
    "    print(data.toarray())\n",
    "    return None\n",
    "\n",
    "\n",
    "tfidfvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T13:22:48.185503Z",
     "start_time": "2025-01-16T13:22:48.178427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'generator'>\n",
      "--------------------------------------------------\n",
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝对', '大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "--------------------------------------------------\n",
      "今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝对 大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。 我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。 如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。\n",
      "['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '大部分' '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个'\n",
      " '看到' '真正' '秘密' '绝对' '美好' '联系' '过去' '这样']\n",
      "--------------------------------------------------\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n",
      "[[0.         0.         0.21821789 0.         0.         0.\n",
      "  0.43643578 0.         0.         0.         0.         0.\n",
      "  0.21821789 0.         0.21821789 0.         0.         0.\n",
      "  0.         0.21821789 0.21821789 0.         0.43643578 0.\n",
      "  0.21821789 0.         0.43643578 0.21821789 0.         0.\n",
      "  0.         0.21821789 0.21821789 0.         0.         0.        ]\n",
      " [0.         0.         0.         0.2410822  0.         0.\n",
      "  0.         0.2410822  0.2410822  0.2410822  0.         0.\n",
      "  0.         0.         0.         0.         0.         0.2410822\n",
      "  0.55004769 0.         0.         0.         0.         0.2410822\n",
      "  0.         0.         0.         0.         0.48216441 0.\n",
      "  0.         0.         0.         0.         0.2410822  0.2410822 ]\n",
      " [0.15698297 0.15698297 0.         0.         0.62793188 0.47094891\n",
      "  0.         0.         0.         0.         0.15698297 0.15698297\n",
      "  0.         0.15698297 0.         0.15698297 0.15698297 0.\n",
      "  0.1193896  0.         0.         0.15698297 0.         0.\n",
      "  0.         0.15698297 0.         0.         0.         0.31396594\n",
      "  0.15698297 0.         0.         0.15698297 0.         0.        ]]\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "source": [
    "def mm():\n",
    "    mm = MinMaxScaler(feature_range=(0, 1))\n",
    "    data = mm.fit_transform([[90, 2, 10, 40], [60, 4, 15, 45], [75, 3, 13, 46]])\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    out = mm.transform([[1, 2, 3, 4], [6, 5, 8, 7]])\n",
    "    print(out)\n",
    "    return None\n",
    "\n",
    "\n",
    "mm()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T13:49:05.049992Z",
     "start_time": "2025-01-16T13:49:05.045426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.         0.         0.        ]\n",
      " [0.         1.         1.         0.83333333]\n",
      " [0.5        0.5        0.6        1.        ]]\n",
      "--------------------------------------------------\n",
      "[[-1.96666667  0.         -1.4        -6.        ]\n",
      " [-1.8         1.5        -0.4        -5.5       ]]\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "source": [
    "def stand():\n",
    "    std = StandardScaler()\n",
    "    data = std.fit_transform([[1., -1., 3.], [2., 4., 2.], [4., 6., -1.]])\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print(std.mean_)\n",
    "    print('-' * 50)\n",
    "    print(std.var_)\n",
    "    print('-' * 50)\n",
    "    print(std.n_samples_seen_)\n",
    "    return data\n",
    "\n",
    "\n",
    "data = stand()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-02-15T07:08:03.258867Z",
     "start_time": "2025-02-15T07:08:03.251937Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.06904497 -1.35873244  0.98058068]\n",
      " [-0.26726124  0.33968311  0.39223227]\n",
      " [ 1.33630621  1.01904933 -1.37281295]]\n",
      "--------------------------------------------------\n",
      "[2.33333333 3.         1.33333333]\n",
      "--------------------------------------------------\n",
      "[1.55555556 8.66666667 2.88888889]\n",
      "--------------------------------------------------\n",
      "3\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "source": [
    "type(data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-16T14:20:18.226463Z",
     "start_time": "2025-01-16T14:20:18.223001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "source": [
    "std1 = StandardScaler()\n",
    "data1 = std1.fit_transform(data)\n",
    "print(np.round(std1.mean_,3))\n",
    "print(std1.var_)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-15T07:25:52.750379Z",
     "start_time": "2025-02-15T07:25:52.746623Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.  0.  0.]\n",
      "[1. 1. 1.]\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "source": [
    "def im():\n",
    "    im = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "    data = im.fit_transform([[1, 2], [np.nan, 3], [7, 6], [3, 2]])\n",
    "    print(data)\n",
    "    return None\n",
    "\n",
    "\n",
    "im()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T14:52:39.957060Z",
     "start_time": "2025-01-16T14:52:39.953211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         2.        ]\n",
      " [3.66666667 3.        ]\n",
      " [7.         6.        ]\n",
      " [3.         2.        ]]\n"
     ]
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "source": [
    "def var():\n",
    "    var = VarianceThreshold(threshold=0.1)\n",
    "\n",
    "    data = var.fit_transform([[0, 2, 0, 3],\n",
    "                              [0, 1, 4, 3],\n",
    "                              [0, 1, 1, 3]])\n",
    "    print(data)\n",
    "    print('-' * 50)\n",
    "    print('The surport is %s' % var.get_support(True))\n",
    "    return None\n",
    "\n",
    "\n",
    "var()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-16T15:03:45.203627Z",
     "start_time": "2025-01-16T15:03:45.199754Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 0]\n",
      " [1 4]\n",
      " [1 1]]\n",
      "--------------------------------------------------\n",
      "The surport is [1 2]\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "source": [
    "def pca():\n",
    "    original_value = np.array([[2, 8, 4, 5], [6, 3, 0, 8], [5, 4, 9, 1]])\n",
    "    print(np.var(original_value, axis=0))\n",
    "    print('-' * 50)\n",
    "    print(np.var(original_value, axis=0).sum())\n",
    "    print('-' * 50)\n",
    "    pca = PCA(n_components=0.9)\n",
    "    data = pca.fit_transform(original_value)\n",
    "    print(data)\n",
    "    print(type(data))\n",
    "    print(np.var(data, axis=0).sum())\n",
    "    print('-' * 50)\n",
    "    print(pca.explained_variance_ratio_)\n",
    "    print(pca.explained_variance_ratio_.sum())\n",
    "    return None\n",
    "\n",
    "\n",
    "pca()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-17T07:53:26.693641Z",
     "start_time": "2025-01-17T07:53:26.688360Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.88888889  4.66666667 13.55555556  8.22222222]\n",
      "--------------------------------------------------\n",
      "29.333333333333336\n",
      "--------------------------------------------------\n",
      "[[-1.28620952e-15  3.82970843e+00]\n",
      " [-5.74456265e+00 -1.91485422e+00]\n",
      " [ 5.74456265e+00 -1.91485422e+00]]\n",
      "<class 'numpy.ndarray'>\n",
      "29.333333333333332\n",
      "--------------------------------------------------\n",
      "[0.75 0.25]\n",
      "1.0\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x = np.random.rand(10000)\n",
    "t = np.arange(len(x))\n",
    "plt.plot(t, x, 'g.', label=\"Uniform Distribution\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-17T08:07:40.197377Z",
     "start_time": "2025-01-17T08:07:40.122249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
